diff --git a/freqtrade/data/dataprovider.py b/freqtrade/data/dataprovider.py
index 1a0903516..4d7296ee7 100644
--- a/freqtrade/data/dataprovider.py
+++ b/freqtrade/data/dataprovider.py
@@ -208,8 +208,10 @@ class DataProvider:
         if saved_pair not in self.__cached_pairs_backtesting:
             timerange = TimeRange.parse_timerange(None if self._config.get(
                 'timerange') is None else str(self._config.get('timerange')))
-            # Move informative start time respecting startup_candle_count
-            startup_candles = self.get_required_startup(str(timeframe))
+
+            # It is not necessary to add the training candles, as they
+            # were already added at the beginning of the backtest.
+            startup_candles = self.get_required_startup(str(timeframe), False)
             tf_seconds = timeframe_to_seconds(str(timeframe))
             timerange.subtract_start(tf_seconds * startup_candles)
             self.__cached_pairs_backtesting[saved_pair] = load_pair_history(
@@ -223,7 +225,7 @@ class DataProvider:
             )
         return self.__cached_pairs_backtesting[saved_pair].copy()
 
-    def get_required_startup(self, timeframe: str) -> int:
+    def get_required_startup(self, timeframe: str, add_train_candles: bool = True) -> int:
         freqai_config = self._config.get('freqai', {})
         if not freqai_config.get('enabled', False):
             return self._config.get('startup_candle_count', 0)
@@ -233,7 +235,9 @@ class DataProvider:
             # make sure the startupcandles is at least the set maximum indicator periods
             self._config['startup_candle_count'] = max(startup_candles, max(indicator_periods))
             tf_seconds = timeframe_to_seconds(timeframe)
-            train_candles = freqai_config['train_period_days'] * 86400 / tf_seconds
+            train_candles = 0
+            if add_train_candles:
+                train_candles = freqai_config['train_period_days'] * 86400 / tf_seconds
             total_candles = int(self._config['startup_candle_count'] + train_candles)
             logger.info(f'Increasing startup_candle_count for freqai to {total_candles}')
             return total_candles
diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py
index 005005368..752cd0e45 100644
--- a/freqtrade/freqai/data_kitchen.py
+++ b/freqtrade/freqai/data_kitchen.py
@@ -466,27 +466,6 @@ class FreqaiDataKitchen:
 
         return df
 
-    def remove_training_from_backtesting(
-        self
-    ) -> DataFrame:
-        """
-        Function which takes the backtesting time range and
-        remove training data from dataframe, keeping only the
-        startup_candle_count candles
-        """
-        startup_candle_count = self.config.get('startup_candle_count', 0)
-        tf = self.config['timeframe']
-        tr = self.config["timerange"]
-
-        backtesting_timerange = TimeRange.parse_timerange(tr)
-        if startup_candle_count > 0 and backtesting_timerange:
-            backtesting_timerange.subtract_start(timeframe_to_seconds(tf) * startup_candle_count)
-
-        start = datetime.fromtimestamp(backtesting_timerange.startts, tz=timezone.utc)
-        df = self.return_dataframe
-        df = df.loc[df["date"] >= start, :]
-        return df
-
     def principal_component_analysis(self) -> None:
         """
         Performs Principal Component Analysis on the data for dimensionality reduction
@@ -994,8 +973,6 @@ class FreqaiDataKitchen:
 
         to_keep = [col for col in dataframe.columns if not col.startswith("&")]
         self.return_dataframe = pd.concat([dataframe[to_keep], self.full_df], axis=1)
-
-        self.return_dataframe = self.remove_training_from_backtesting()
         self.full_df = DataFrame()
 
         return
diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py
index 9ba610b69..2a1c44f7f 100644
--- a/freqtrade/optimize/backtesting.py
+++ b/freqtrade/optimize/backtesting.py
@@ -139,9 +139,14 @@ class Backtesting:
 
         # Get maximum required startup period
         self.required_startup = max([strat.startup_candle_count for strat in self.strategylist])
+        self.exchange.validate_required_startup_candles(self.required_startup, self.timeframe)
+
+        if self.config.get('freqai', {}).get('enabled', False):
+            # For FreqAI, increase the required_startup to includes the training data
+            self.required_startup = self.dataprovider.get_required_startup(self.timeframe)
+
         # Add maximum startup candle count to configuration for informative pairs support
         self.config['startup_candle_count'] = self.required_startup
-        self.exchange.validate_required_startup_candles(self.required_startup, self.timeframe)
 
         self.trading_mode: TradingMode = config.get('trading_mode', TradingMode.SPOT)
         # strategies which define "can_short=True" will fail to load in Spot mode.
@@ -217,7 +222,7 @@ class Backtesting:
             pairs=self.pairlists.whitelist,
             timeframe=self.timeframe,
             timerange=self.timerange,
-            startup_candles=self.dataprovider.get_required_startup(self.timeframe),
+            startup_candles=self.config['startup_candle_count'],
             fail_without_data=True,
             data_format=self.config.get('dataformat_ohlcv', 'json'),
             candle_type=self.config.get('candle_type_def', CandleType.SPOT)